847 lines (846 with data), 174.5 kB

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "A7xgHxPxd0J_",
        "outputId": "2c065005-4713-4e7f-f035-98b163cb2d14"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since beginning of run: 1706000118.3307664\n",
            "Tue Jan 23 08:55:18 2024\n"
          ]
        }
      ],
      "source": [
        "# This cell is added by sphinx-gallery\n",
        "# It can be customized to whatever you like\n",
        "%matplotlib inline\n",
        "# from google.colab import drive\n",
        "# drive.mount('/content/drive')\n",
        "# !pip install pennylane\n",
        "import time\n",
        "seconds = time.time()\n",
        "print(\"Time in seconds since beginning of run:\", seconds)\n",
        "local_time = time.ctime(seconds)\n",
        "print(local_time)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RWYw4Yy7d0KA"
      },
      "source": [
        "Quanvolutional Neural Networks {#quanvolution}\n",
        "==============================\n",
        "\n",
        "::: {.meta}\n",
        ":property=\\\"og:description\\\": Train a quantum convolutional neural\n",
        "network to classify MNIST images. :property=\\\"og:image\\\":\n",
        "<https://pennylane.ai/qml/_static/demonstration_assets//circuit.png>\n",
        ":::\n",
        "\n",
        "*Author: Andrea Mari --- Posted: 24 March 2020. Last updated: 15 January\n",
        "2021.*\n",
        "\n",
        "In this demo we implement the *Quanvolutional Neural Network*, a quantum\n",
        "machine learning model originally introduced in [Henderson et al.\n",
        "(2019)](https://arxiv.org/abs/1904.04767).\n",
        "\n",
        "![](../_static/demonstration_assets/quanvolution/circuit.png){.align-center\n",
        "width=\"90.0%\"}\n",
        "\n",
        "Introduction\n",
        "------------\n",
        "\n",
        "### Classical convolution\n",
        "\n",
        "The *convolutional neural network* (CNN) is a standard model in\n",
        "classical machine learning which is particularly suitable for processing\n",
        "images. The model is based on the idea of a *convolution layer* where,\n",
        "instead of processing the full input data with a global function, a\n",
        "local convolution is applied.\n",
        "\n",
        "If the input is an image, small local regions are sequentially processed\n",
        "with the same kernel. The results obtained for each region are usually\n",
        "associated to different channels of a single output pixel. The union of\n",
        "all the output pixels produces a new image-like object, which can be\n",
        "further processed by additional layers.\n",
        "\n",
        "### Quantum convolution\n",
        "\n",
        "One can extend the same idea also to the context of quantum variational\n",
        "circuits. A possible approach is given by the following procedure which\n",
        "is very similar to the one used in Ref. \\[1\\]. The scheme is also\n",
        "represented in the figure at the top of this tutorial.\n",
        "\n",
        "1.  A small region of the input image, in our example a $2 \\times 2$\n",
        "    square, is embedded into a quantum circuit. In this demo, this is\n",
        "    achieved with parametrized rotations applied to the qubits\n",
        "    initialized in the ground state.\n",
        "2.  A quantum computation, associated to a unitary $U$, is performed on\n",
        "    the system. The unitary could be generated by a variational quantum\n",
        "    circuit or, more simply, by a random circuit as proposed in Ref.\n",
        "    \\[1\\].\n",
        "3.  The quantum system is finally measured, obtaining a list of\n",
        "    classical expectation values. The measurement results could also be\n",
        "    classically post-processed as proposed in Ref. \\[1\\] but, for\n",
        "    simplicity, in this demo we directly use the raw expectation values.\n",
        "4.  Analogously to a classical convolution layer, each expectation value\n",
        "    is mapped to a different channel of a single output pixel.\n",
        "5.  Iterating the same procedure over different regions, one can scan\n",
        "    the full input image, producing an output object which will be\n",
        "    structured as a multi-channel image.\n",
        "6.  The quantum convolution can be followed by further quantum layers or\n",
        "    by classical layers.\n",
        "\n",
        "The main difference with respect to a classical convolution is that a\n",
        "quantum circuit can generate highly complex kernels whose computation\n",
        "could be, at least in principle, classically intractable.\n",
        "\n",
        "::: {.note}\n",
        "::: {.title}\n",
        "Note\n",
        ":::\n",
        "\n",
        "In this tutorial we follow the approach of Ref. \\[1\\] in which a fixed\n",
        "non-trainable quantum circuit is used as a \\\"quanvolution\\\" kernel,\n",
        "while the subsequent classical layers are trained for the classification\n",
        "problem of interest. However, by leveraging the ability of PennyLane to\n",
        "evaluate gradients of quantum circuits, the quantum kernel could also be\n",
        "trained.\n",
        ":::\n",
        "\n",
        "General setup\n",
        "-------------\n",
        "\n",
        "This Python code requires *PennyLane* with the *TensorFlow* interface\n",
        "and the plotting library *matplotlib*.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "VPbcKloNd0KC"
      },
      "outputs": [],
      "source": [
        "import pennylane as qml\n",
        "from pennylane import numpy as np\n",
        "from pennylane.templates import RandomLayers\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ISZAWXDMd0KC"
      },
      "source": [
        "Setting of the main hyper-parameters of the model\n",
        "=================================================\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "1D5V-v5Vd0KC"
      },
      "outputs": [],
      "source": [
        "n_epochs = 30   # Number of optimization epochs\n",
        "n_layers = 1    # Number of random layers\n",
        "n_train = 50    # Size of the train dataset\n",
        "n_test = 30     # Size of the test dataset\n",
        "\n",
        "SAVE_PATH = \"/content/drive/MyDrive/Colab Notebooks/data/quanvolution\"  # Data saving folder\n",
        "PREPROCESS = True           # If False, skip quantum processing and load data from SAVE_PATH\n",
        "np.random.seed(0)           # Seed for NumPy random number generator\n",
        "tf.random.set_seed(0)       # Seed for TensorFlow random number generator"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "avddR0atd0KC"
      },
      "source": [
        "Loading of the MNIST dataset\n",
        "============================\n",
        "\n",
        "We import the MNIST dataset from *Keras*. To speedup the evaluation of\n",
        "this demo we use only a small number of training and test images.\n",
        "Obviously, better results are achievable when using the full dataset.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "xlhnv1hrd0KC"
      },
      "outputs": [],
      "source": [
        "mnist_dataset = keras.datasets.mnist\n",
        "(train_images, train_labels), (test_images, test_labels) = mnist_dataset.load_data()\n",
        "\n",
        "# Reduce dataset size\n",
        "train_images = train_images[:n_train]\n",
        "train_labels = train_labels[:n_train]\n",
        "test_images = test_images[:n_test]\n",
        "test_labels = test_labels[:n_test]\n",
        "\n",
        "# Normalize pixel values within 0 and 1\n",
        "train_images = train_images / 255\n",
        "test_images = test_images / 255\n",
        "\n",
        "# Add extra dimension for convolution channels\n",
        "train_images = np.array(train_images[..., tf.newaxis], requires_grad=False)\n",
        "test_images = np.array(test_images[..., tf.newaxis], requires_grad=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bDVPKQD8d0KD"
      },
      "source": [
        "Quantum circuit as a convolution kernel\n",
        "=======================================\n",
        "\n",
        "We follow the scheme described in the introduction and represented in\n",
        "the figure at the top of this demo.\n",
        "\n",
        "We initialize a PennyLane `default.qubit` device, simulating a system of\n",
        "$4$ qubits. The associated `qnode` represents the quantum circuit\n",
        "consisting of:\n",
        "\n",
        "1.  an embedding layer of local $R_y$ rotations (with angles scaled by a\n",
        "    factor of $\\pi$);\n",
        "2.  a random circuit of `n_layers`;\n",
        "3.  a final measurement in the computational basis, estimating $4$\n",
        "    expectation values.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "rD5_3eztd0KD"
      },
      "outputs": [],
      "source": [
        "dev = qml.device(\"default.qubit\", wires=4)\n",
        "# Random circuit parameters\n",
        "rand_params = np.random.uniform(high=2 * np.pi, size=(n_layers, 4))\n",
        "\n",
        "@qml.qnode(dev)\n",
        "def circuit(phi):\n",
        "    # Encoding of 4 classical input values\n",
        "    for j in range(4):\n",
        "        qml.RY(np.pi * phi[j], wires=j)\n",
        "    for j in range(4):\n",
        "        qml.RY(np.pi * phi[j], wires=j)\n",
        "\n",
        "    # Measurement producing 4 classical output values\n",
        "    return [qml.expval(qml.PauliZ(j)) for j in range(4)]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "02g-DOe8d0KD"
      },
      "source": [
        "The next function defines the convolution scheme:\n",
        "\n",
        "1.  the image is divided into squares of $2 \\times 2$ pixels;\n",
        "2.  each square is processed by the quantum circuit;\n",
        "3.  the $4$ expectation values are mapped into $4$ different channels of\n",
        "    a single output pixel.\n",
        "\n",
        "::: {.note}\n",
        "::: {.title}\n",
        "Note\n",
        ":::\n",
        "\n",
        "This process halves the resolution of the input image. In the standard\n",
        "language of CNN, this would correspond to a convolution with a\n",
        "$2 \\times 2$ *kernel* and a *stride* equal to $2$.\n",
        ":::\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "AEL9cTFEd0KD"
      },
      "outputs": [],
      "source": [
        "def quanv(image):\n",
        "    \"\"\"Convolves the input image with many applications of the same quantum circuit.\"\"\"\n",
        "    out = np.zeros((14, 14, 4))\n",
        "\n",
        "    # Loop over the coordinates of the top-left pixel of 2X2 squares\n",
        "    for j in range(0, 28, 2):\n",
        "        for k in range(0, 28, 2):\n",
        "            # Process a squared 2x2 region of the image with a quantum circuit\n",
        "            q_results = circuit(\n",
        "                [\n",
        "                    image[j, k, 0],\n",
        "                    image[j, k + 1, 0],\n",
        "                    image[j + 1, k, 0],\n",
        "                    image[j + 1, k + 1, 0]\n",
        "                ]\n",
        "            )\n",
        "            # Assign expectation values to different channels of the output pixel (j/2, k/2)\n",
        "            for c in range(4):\n",
        "                out[j // 2, k // 2, c] = q_results[c]\n",
        "    return out"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N3MmyCQad0KD"
      },
      "source": [
        "Quantum pre-processing of the dataset\n",
        "=====================================\n",
        "\n",
        "Since we are not going to train the quantum convolution layer, it is\n",
        "more efficient to apply it as a \\\"pre-processing\\\" layer to all the\n",
        "images of our dataset. Later an entirely classical model will be\n",
        "directly trained and tested on the pre-processed dataset, avoiding\n",
        "unnecessary repetitions of quantum computations.\n",
        "\n",
        "The pre-processed images will be saved in the folder `SAVE_PATH`. Once\n",
        "saved, they can be directly loaded by setting `PREPROCESS = False`,\n",
        "otherwise the quantum convolution is evaluated at each run of the code.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "c3oexS3hd0KD",
        "outputId": "58648a87-fed3-4096-96a9-c3561bd56adc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Quantum pre-processing of train images:\n",
            "\n",
            "Quantum pre-processing of test images:\n"
          ]
        }
      ],
      "source": [
        "if PREPROCESS == True:\n",
        "    q_train_images = []\n",
        "    print(\"Quantum pre-processing of train images:\")\n",
        "    for idx, img in enumerate(train_images):\n",
        "        print(\"{}/{}        \".format(idx + 1, n_train), end=\"\\r\")\n",
        "        q_train_images.append(quanv(img))\n",
        "    q_train_images = np.asarray(q_train_images)\n",
        "\n",
        "    q_test_images = []\n",
        "    print(\"\\nQuantum pre-processing of test images:\")\n",
        "    for idx, img in enumerate(test_images):\n",
        "        print(\"{}/{}        \".format(idx + 1, n_test), end=\"\\r\")\n",
        "        q_test_images.append(quanv(img))\n",
        "    q_test_images = np.asarray(q_test_images)\n",
        "\n",
        "    # Save pre-processed images\n",
        "    np.save(SAVE_PATH + \"q_train_images.npy\", q_train_images)\n",
        "    np.save(SAVE_PATH + \"q_test_images.npy\", q_test_images)\n",
        "\n",
        "\n",
        "# Load pre-processed images\n",
        "q_train_images = np.load(SAVE_PATH + \"q_train_images.npy\")\n",
        "q_test_images = np.load(SAVE_PATH + \"q_test_images.npy\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kJYilWS1d0KE"
      },
      "source": [
        "Let us visualize the effect of the quantum convolution layer on a batch\n",
        "of samples:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1006
        },
        "id": "2ckiL7srd0KE",
        "outputId": "0e0b5200-1acc-47e7-a962-134dbfa502ef"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 20 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "n_samples = 4\n",
        "n_channels = 4\n",
        "fig, axes = plt.subplots(1 + n_channels, n_samples, figsize=(10, 10))\n",
        "for k in range(n_samples):\n",
        "    axes[0, 0].set_ylabel(\"Input\")\n",
        "    if k != 0:\n",
        "        axes[0, k].yaxis.set_visible(False)\n",
        "    axes[0, k].imshow(train_images[k, :, :, 0], cmap=\"gray\")\n",
        "\n",
        "    # Plot all output channels\n",
        "    for c in range(n_channels):\n",
        "        axes[c + 1, 0].set_ylabel(\"Output [ch. {}]\".format(c))\n",
        "        if k != 0:\n",
        "            axes[c, k].yaxis.set_visible(False)\n",
        "        axes[c + 1, k].imshow(q_train_images[k, :, :, c], cmap=\"gray\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rCnk_yy0d0KE"
      },
      "source": [
        "Below each input image, the $4$ output channels generated by the quantum\n",
        "convolution are visualized in gray scale.\n",
        "\n",
        "One can clearly notice the downsampling of the resolution and some local\n",
        "distortion introduced by the quantum kernel. On the other hand the\n",
        "global shape of the image is preserved, as expected for a convolution\n",
        "layer.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vCjr6WvXd0KE"
      },
      "source": [
        "Hybrid quantum-classical model\n",
        "==============================\n",
        "\n",
        "After the application of the quantum convolution layer we feed the\n",
        "resulting features into a classical neural network that will be trained\n",
        "to classify the $10$ different digits of the MNIST dataset.\n",
        "\n",
        "We use a very simple model: just a fully connected layer with 10 output\n",
        "nodes with a final *softmax* activation function.\n",
        "\n",
        "The model is compiled with a *stochastic-gradient-descent* optimizer,\n",
        "and a *cross-entropy* loss function.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "PHtPdynad0KE"
      },
      "outputs": [],
      "source": [
        "def MyModel():\n",
        "    \"\"\"Initializes and returns a custom Keras model\n",
        "    which is ready to be trained.\"\"\"\n",
        "    model = keras.models.Sequential([\n",
        "        keras.layers.Flatten(),\n",
        "        keras.layers.Dense(10, activation=\"softmax\")\n",
        "    ])\n",
        "\n",
        "    model.compile(\n",
        "        optimizer='adam',\n",
        "        loss=\"sparse_categorical_crossentropy\",\n",
        "        metrics=[\"accuracy\"],\n",
        "    )\n",
        "    return model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WlOj9hwGd0KE"
      },
      "source": [
        "Training\n",
        "========\n",
        "\n",
        "We first initialize an instance of the model, then we train and validate\n",
        "it with the dataset that has been already pre-processed by a quantum\n",
        "convolution.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Zj8fE8uhd0KF",
        "outputId": "65ae70e1-1f11-4dbc-be7b-e0ef4a64cf4f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "13/13 - 1s - loss: 3.1497 - accuracy: 0.0800 - val_loss: 2.4975 - val_accuracy: 0.0333 - 1s/epoch - 77ms/step\n",
            "Epoch 2/30\n",
            "13/13 - 0s - loss: 2.4949 - accuracy: 0.1800 - val_loss: 2.4020 - val_accuracy: 0.1667 - 59ms/epoch - 5ms/step\n",
            "Epoch 3/30\n",
            "13/13 - 0s - loss: 2.3435 - accuracy: 0.1800 - val_loss: 2.4355 - val_accuracy: 0.1000 - 58ms/epoch - 4ms/step\n",
            "Epoch 4/30\n",
            "13/13 - 0s - loss: 1.9775 - accuracy: 0.3000 - val_loss: 2.3018 - val_accuracy: 0.1667 - 59ms/epoch - 5ms/step\n",
            "Epoch 5/30\n",
            "13/13 - 0s - loss: 1.9126 - accuracy: 0.3800 - val_loss: 2.2700 - val_accuracy: 0.1333 - 59ms/epoch - 5ms/step\n",
            "Epoch 6/30\n",
            "13/13 - 0s - loss: 1.7907 - accuracy: 0.3600 - val_loss: 2.3897 - val_accuracy: 0.1000 - 58ms/epoch - 4ms/step\n",
            "Epoch 7/30\n",
            "13/13 - 0s - loss: 1.5564 - accuracy: 0.5200 - val_loss: 2.1725 - val_accuracy: 0.3000 - 59ms/epoch - 5ms/step\n",
            "Epoch 8/30\n",
            "13/13 - 0s - loss: 1.4456 - accuracy: 0.6200 - val_loss: 2.2610 - val_accuracy: 0.1333 - 56ms/epoch - 4ms/step\n",
            "Epoch 9/30\n",
            "13/13 - 0s - loss: 1.2652 - accuracy: 0.8000 - val_loss: 2.1684 - val_accuracy: 0.3000 - 56ms/epoch - 4ms/step\n",
            "Epoch 10/30\n",
            "13/13 - 0s - loss: 1.0837 - accuracy: 0.9000 - val_loss: 2.2889 - val_accuracy: 0.2333 - 58ms/epoch - 4ms/step\n",
            "Epoch 11/30\n",
            "13/13 - 0s - loss: 1.0262 - accuracy: 0.9000 - val_loss: 2.1929 - val_accuracy: 0.2667 - 53ms/epoch - 4ms/step\n",
            "Epoch 12/30\n",
            "13/13 - 0s - loss: 0.9698 - accuracy: 0.9000 - val_loss: 2.2107 - val_accuracy: 0.2000 - 56ms/epoch - 4ms/step\n",
            "Epoch 13/30\n",
            "13/13 - 0s - loss: 0.8134 - accuracy: 0.9800 - val_loss: 2.1255 - val_accuracy: 0.2667 - 54ms/epoch - 4ms/step\n",
            "Epoch 14/30\n",
            "13/13 - 0s - loss: 0.7967 - accuracy: 0.9000 - val_loss: 2.1849 - val_accuracy: 0.2333 - 55ms/epoch - 4ms/step\n",
            "Epoch 15/30\n",
            "13/13 - 0s - loss: 0.6910 - accuracy: 0.9600 - val_loss: 2.0876 - val_accuracy: 0.4000 - 55ms/epoch - 4ms/step\n",
            "Epoch 16/30\n",
            "13/13 - 0s - loss: 0.6059 - accuracy: 0.9800 - val_loss: 2.1603 - val_accuracy: 0.3333 - 57ms/epoch - 4ms/step\n",
            "Epoch 17/30\n",
            "13/13 - 0s - loss: 0.5749 - accuracy: 1.0000 - val_loss: 2.1219 - val_accuracy: 0.2667 - 55ms/epoch - 4ms/step\n",
            "Epoch 18/30\n",
            "13/13 - 0s - loss: 0.5072 - accuracy: 0.9800 - val_loss: 2.2010 - val_accuracy: 0.2333 - 56ms/epoch - 4ms/step\n",
            "Epoch 19/30\n",
            "13/13 - 0s - loss: 0.5034 - accuracy: 1.0000 - val_loss: 2.0479 - val_accuracy: 0.3333 - 61ms/epoch - 5ms/step\n",
            "Epoch 20/30\n",
            "13/13 - 0s - loss: 0.4544 - accuracy: 0.9800 - val_loss: 2.0717 - val_accuracy: 0.3000 - 61ms/epoch - 5ms/step\n",
            "Epoch 21/30\n",
            "13/13 - 0s - loss: 0.3926 - accuracy: 1.0000 - val_loss: 2.2323 - val_accuracy: 0.3333 - 64ms/epoch - 5ms/step\n",
            "Epoch 22/30\n",
            "13/13 - 0s - loss: 0.3714 - accuracy: 1.0000 - val_loss: 1.9572 - val_accuracy: 0.4667 - 59ms/epoch - 5ms/step\n",
            "Epoch 23/30\n",
            "13/13 - 0s - loss: 0.3389 - accuracy: 1.0000 - val_loss: 2.0842 - val_accuracy: 0.3333 - 57ms/epoch - 4ms/step\n",
            "Epoch 24/30\n",
            "13/13 - 0s - loss: 0.3059 - accuracy: 1.0000 - val_loss: 2.0748 - val_accuracy: 0.3667 - 56ms/epoch - 4ms/step\n",
            "Epoch 25/30\n",
            "13/13 - 0s - loss: 0.2989 - accuracy: 1.0000 - val_loss: 2.0465 - val_accuracy: 0.4667 - 57ms/epoch - 4ms/step\n",
            "Epoch 26/30\n",
            "13/13 - 0s - loss: 0.2753 - accuracy: 1.0000 - val_loss: 2.0523 - val_accuracy: 0.3667 - 57ms/epoch - 4ms/step\n",
            "Epoch 27/30\n",
            "13/13 - 0s - loss: 0.2466 - accuracy: 1.0000 - val_loss: 1.9938 - val_accuracy: 0.4333 - 57ms/epoch - 4ms/step\n",
            "Epoch 28/30\n",
            "13/13 - 0s - loss: 0.2430 - accuracy: 1.0000 - val_loss: 2.0696 - val_accuracy: 0.4667 - 61ms/epoch - 5ms/step\n",
            "Epoch 29/30\n",
            "13/13 - 0s - loss: 0.2286 - accuracy: 1.0000 - val_loss: 2.0574 - val_accuracy: 0.3667 - 59ms/epoch - 5ms/step\n",
            "Epoch 30/30\n",
            "13/13 - 0s - loss: 0.2089 - accuracy: 1.0000 - val_loss: 2.0005 - val_accuracy: 0.4000 - 59ms/epoch - 5ms/step\n"
          ]
        }
      ],
      "source": [
        "q_model = MyModel()\n",
        "\n",
        "q_history = q_model.fit(\n",
        "    q_train_images,\n",
        "    train_labels,\n",
        "    validation_data=(q_test_images, test_labels),\n",
        "    batch_size=4,\n",
        "    epochs=n_epochs,\n",
        "    verbose=2,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZrgNg_sSd0KF"
      },
      "source": [
        "In order to compare the results achievable with and without the quantum\n",
        "convolution layer, we initialize also a \\\"classical\\\" instance of the\n",
        "model that will be directly trained and validated with the raw MNIST\n",
        "images (i.e., without quantum pre-processing).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "xVJR8xXcd0KF",
        "outputId": "722b0d82-6d24-47bd-94e7-6a65f3cdd0f3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "13/13 - 1s - loss: 2.4174 - accuracy: 0.0800 - val_loss: 2.0614 - val_accuracy: 0.3000 - 591ms/epoch - 45ms/step\n",
            "Epoch 2/30\n",
            "13/13 - 0s - loss: 2.0223 - accuracy: 0.2800 - val_loss: 1.9271 - val_accuracy: 0.4333 - 57ms/epoch - 4ms/step\n",
            "Epoch 3/30\n",
            "13/13 - 0s - loss: 1.7162 - accuracy: 0.5600 - val_loss: 1.8124 - val_accuracy: 0.4667 - 59ms/epoch - 5ms/step\n",
            "Epoch 4/30\n",
            "13/13 - 0s - loss: 1.4844 - accuracy: 0.7600 - val_loss: 1.6963 - val_accuracy: 0.5667 - 59ms/epoch - 5ms/step\n",
            "Epoch 5/30\n",
            "13/13 - 0s - loss: 1.2820 - accuracy: 0.8200 - val_loss: 1.5844 - val_accuracy: 0.6333 - 56ms/epoch - 4ms/step\n",
            "Epoch 6/30\n",
            "13/13 - 0s - loss: 1.1197 - accuracy: 0.8800 - val_loss: 1.4951 - val_accuracy: 0.6333 - 58ms/epoch - 4ms/step\n",
            "Epoch 7/30\n",
            "13/13 - 0s - loss: 0.9770 - accuracy: 0.9400 - val_loss: 1.4278 - val_accuracy: 0.6667 - 56ms/epoch - 4ms/step\n",
            "Epoch 8/30\n",
            "13/13 - 0s - loss: 0.8568 - accuracy: 0.9600 - val_loss: 1.3636 - val_accuracy: 0.7333 - 58ms/epoch - 4ms/step\n",
            "Epoch 9/30\n",
            "13/13 - 0s - loss: 0.7574 - accuracy: 0.9800 - val_loss: 1.3048 - val_accuracy: 0.7333 - 56ms/epoch - 4ms/step\n",
            "Epoch 10/30\n",
            "13/13 - 0s - loss: 0.6707 - accuracy: 0.9800 - val_loss: 1.2660 - val_accuracy: 0.7667 - 65ms/epoch - 5ms/step\n",
            "Epoch 11/30\n",
            "13/13 - 0s - loss: 0.6029 - accuracy: 0.9800 - val_loss: 1.2231 - val_accuracy: 0.7667 - 59ms/epoch - 5ms/step\n",
            "Epoch 12/30\n",
            "13/13 - 0s - loss: 0.5432 - accuracy: 0.9800 - val_loss: 1.2079 - val_accuracy: 0.7667 - 57ms/epoch - 4ms/step\n",
            "Epoch 13/30\n",
            "13/13 - 0s - loss: 0.4900 - accuracy: 1.0000 - val_loss: 1.1784 - val_accuracy: 0.7667 - 56ms/epoch - 4ms/step\n",
            "Epoch 14/30\n",
            "13/13 - 0s - loss: 0.4414 - accuracy: 1.0000 - val_loss: 1.1394 - val_accuracy: 0.7667 - 65ms/epoch - 5ms/step\n",
            "Epoch 15/30\n",
            "13/13 - 0s - loss: 0.3996 - accuracy: 1.0000 - val_loss: 1.1140 - val_accuracy: 0.7667 - 59ms/epoch - 5ms/step\n",
            "Epoch 16/30\n",
            "13/13 - 0s - loss: 0.3653 - accuracy: 1.0000 - val_loss: 1.0968 - val_accuracy: 0.7667 - 58ms/epoch - 4ms/step\n",
            "Epoch 17/30\n",
            "13/13 - 0s - loss: 0.3366 - accuracy: 1.0000 - val_loss: 1.0812 - val_accuracy: 0.8000 - 60ms/epoch - 5ms/step\n",
            "Epoch 18/30\n",
            "13/13 - 0s - loss: 0.3098 - accuracy: 1.0000 - val_loss: 1.0573 - val_accuracy: 0.8000 - 57ms/epoch - 4ms/step\n",
            "Epoch 19/30\n",
            "13/13 - 0s - loss: 0.2837 - accuracy: 1.0000 - val_loss: 1.0528 - val_accuracy: 0.8000 - 57ms/epoch - 4ms/step\n",
            "Epoch 20/30\n",
            "13/13 - 0s - loss: 0.2635 - accuracy: 1.0000 - val_loss: 1.0337 - val_accuracy: 0.8333 - 56ms/epoch - 4ms/step\n",
            "Epoch 21/30\n",
            "13/13 - 0s - loss: 0.2442 - accuracy: 1.0000 - val_loss: 1.0304 - val_accuracy: 0.8333 - 56ms/epoch - 4ms/step\n",
            "Epoch 22/30\n",
            "13/13 - 0s - loss: 0.2269 - accuracy: 1.0000 - val_loss: 1.0163 - val_accuracy: 0.8000 - 59ms/epoch - 5ms/step\n",
            "Epoch 23/30\n",
            "13/13 - 0s - loss: 0.2114 - accuracy: 1.0000 - val_loss: 1.0080 - val_accuracy: 0.8333 - 60ms/epoch - 5ms/step\n",
            "Epoch 24/30\n",
            "13/13 - 0s - loss: 0.1974 - accuracy: 1.0000 - val_loss: 0.9988 - val_accuracy: 0.7667 - 55ms/epoch - 4ms/step\n",
            "Epoch 25/30\n",
            "13/13 - 0s - loss: 0.1867 - accuracy: 1.0000 - val_loss: 0.9918 - val_accuracy: 0.7667 - 57ms/epoch - 4ms/step\n",
            "Epoch 26/30\n",
            "13/13 - 0s - loss: 0.1739 - accuracy: 1.0000 - val_loss: 0.9856 - val_accuracy: 0.7667 - 59ms/epoch - 5ms/step\n",
            "Epoch 27/30\n",
            "13/13 - 0s - loss: 0.1642 - accuracy: 1.0000 - val_loss: 0.9752 - val_accuracy: 0.7667 - 59ms/epoch - 5ms/step\n",
            "Epoch 28/30\n",
            "13/13 - 0s - loss: 0.1558 - accuracy: 1.0000 - val_loss: 0.9675 - val_accuracy: 0.7667 - 61ms/epoch - 5ms/step\n",
            "Epoch 29/30\n",
            "13/13 - 0s - loss: 0.1462 - accuracy: 1.0000 - val_loss: 0.9669 - val_accuracy: 0.7667 - 59ms/epoch - 5ms/step\n",
            "Epoch 30/30\n",
            "13/13 - 0s - loss: 0.1377 - accuracy: 1.0000 - val_loss: 0.9619 - val_accuracy: 0.7667 - 60ms/epoch - 5ms/step\n"
          ]
        }
      ],
      "source": [
        "c_model = MyModel()\n",
        "\n",
        "c_history = c_model.fit(\n",
        "    train_images,\n",
        "    train_labels,\n",
        "    validation_data=(test_images, test_labels),\n",
        "    batch_size=4,\n",
        "    epochs=n_epochs,\n",
        "    verbose=2,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oXLGIhPJd0KF"
      },
      "source": [
        "Results\n",
        "=======\n",
        "\n",
        "We can finally plot the test accuracy and the test loss with respect to\n",
        "the number of training epochs.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 963
        },
        "id": "fwDEhq0Qd0KF",
        "outputId": "1984198b-d335-4045-b4b0-fe339298b9ba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-38-c3ef9ba498fb>:3: MatplotlibDeprecationWarning: The seaborn styles shipped by Matplotlib are deprecated since 3.6, as they no longer correspond to the styles shipped by seaborn. However, they will remain available as 'seaborn-v0_8-<style>'. Alternatively, directly use the seaborn API instead.\n",
            "  plt.style.use(\"seaborn\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x900 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.style.use(\"seaborn\")\n",
        "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(6, 9))\n",
        "\n",
        "ax1.plot(q_history.history[\"val_accuracy\"], \"-ob\", label=\"With quantum layer\")\n",
        "ax1.plot(c_history.history[\"val_accuracy\"], \"-og\", label=\"Without quantum layer\")\n",
        "ax1.set_ylabel(\"Accuracy\")\n",
        "ax1.set_ylim([0, 1])\n",
        "ax1.set_xlabel(\"Epoch\")\n",
        "ax1.legend()\n",
        "\n",
        "ax2.plot(q_history.history[\"val_loss\"], \"-ob\", label=\"With quantum layer\")\n",
        "ax2.plot(c_history.history[\"val_loss\"], \"-og\", label=\"Without quantum layer\")\n",
        "ax2.set_ylabel(\"Loss\")\n",
        "ax2.set_ylim(top=2.5)\n",
        "ax2.set_xlabel(\"Epoch\")\n",
        "ax2.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eFIiSMV9d0KF"
      },
      "source": [
        "References\n",
        "==========\n",
        "\n",
        "1.  Maxwell Henderson, Samriddhi Shakya, Shashindra Pradhan, Tristan\n",
        "    Cook. \\\"Quanvolutional Neural Networks: Powering Image Recognition\n",
        "    with Quantum Circuits.\\\"\n",
        "    [arXiv:1904.04767](https://arxiv.org/abs/1904.04767), 2019.\n",
        "\n",
        "About the author\n",
        "================\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "seconds = time.time()\n",
        "print(\"Time in seconds since end of run:\", seconds)\n",
        "local_time = time.ctime(seconds)\n",
        "print(local_time)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "DN7xl_1qggGu",
        "outputId": "2c8fe0ae-ef24-4a3b-a0bc-8b64468fcf7f"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time in seconds since end of run: 1706000238.575018\n",
            "Tue Jan 23 08:57:18 2024\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.13"
    },
    "colab": {
      "provenance": [],
      "machine_shape": "hm"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}